What is Machine Learning, How Does it Work and Why is it Important?

Learn what is machine learning, how it works and its importance in five minutes.

April 30, 2019 by Roberto Iriondo

Introduction to Machine Learning | Part 1 | #ML4All

Who should read these article series?

Anyone who is curious and wants a truly simple, yet accurate overview of the definition of machine learning, about how it works and its importance. We will go through each of the pertaining questions raised above by slicing technical definitions from machine learning pioneers and industry leaders as to present you with a true simplistic introduction to the amazing, scientific field of machine learning.

Glossary of terms can be found at the bottom of the article, along with a small set of resources for further learning, references and disclosures.

If the above applies to you, read on!

What is machine learning?

Computer Scientist and machine learning pioneer Tom M. Mitchell Portrayed | Source: Machine Learning, McGraw Hill, 1997, Tom M. Mitchell [2]

The scientific field of machine learning (ML) is a branch of artificial intelligence, as defined by Computer Scientist and machine learning pioneer [1] Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience [2].”

An algorithm can be thought of as a set of rules/instructions that a computer programmer specifies, which a computer is able to process. Simply put, machine learning algorithms learn by experience, similar to how humans do. For example, after having seen multiple examples of an object, a compute-employing machine learning algorithm can become able to recognize that object in new, previously unseen scenarios.

How does machine learning work?

How machine learning works? ~ Yann LeCun, Head of Facebook AI Research | Source: Youtube [3]

In the video above [3], Head of Facebook AI Research, Yann LeCun simply explains how machine learning works with easy to follow examples. Machine learning utilizes a variety of techniques to intelligently handle large and complex amounts of information to make decisions and/or predictions.

In practice, the patterns that a computer (machine learning system) learns can be very complicated and difficult to explain. Consider searching for dog images on Google search — as seen on the image below, Google is incredibly good at bringing relevant results, yet how does Google search achieves this task? In simple terms, Google search first gets a large quantity of examples (image dataset) of photos labeled “dog” — then the computer (machine learning system) looks for patterns of pixels and patterns of colors that will help it guess (predict) if the image queried it is indeed a dog.

Query on Google Search for “dog” | Source: Google Search

At first, Google’s computer makes a random guess of what patterns are good as to identify an image of a dog. If it makes a mistake, then a set of adjustments are made in order for the computer to get it right. In the end, such collection of patterns will be learned by a large computer system modeled after the human brain (deep neural network), that once is trained can correctly identify and bring accurate results of dog images on Google search, along with anything else that you could possibly think of —such process is called the training phase of a machine learning system.

Machine learning system looking for patterns between dog and cat images [5]

Imagine that you were in charge of building a machine learning prediction system to try and identify images between dogs and cats. The first step as we explained above would be to gather a large quantity of labeled images with “dog” for dogs and “cat” for cats. Second, we would train the computer to look for patterns on the images as to identify dogs and cats respectively.

Trained machine learning system capable of identifying cats or dogs. [5]

Once the machine learning model has been trained [7], we can throw at it (input) different images to see if it can correctly identify dogs and cats. As seen on the image above, a trained machine learning model can (most of the time) correctly identify such queries.

Why is machine learning important?

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” ~ Andrew Ng | Source: Stanford Business Graduate School [4]

Machine learning its incredibly important nowadays, Firstly because it can solve complicated real-world problems in a scalable way. Secondly, because it has disrupted a variety of industries within the past decade [9], and will continue to do so in the future, as more and more industry leaders and researchers are specializing in machine learning, along taking what they have learned in order to continue with their research and/or develop machine learning tools to positively impact their own fields. Thirdly, artificial intelligence has the potential to incrementally add 16% or around $ 13 trillion to the US economy by 2030 [18]. The rate in which machine learning is causing positive impact, is already surprisingly impressive [10] [11] [12] [13] [14] [15] [16] which have been successful thanks to the dramatic change on data storage and computing processing power [17] — as more people are increasingly becoming involved, we can only expect it to continue with this route and continue to cause amazing progress on different fields [6].

Future work: In an upcoming article we will discuss the types of machine learning in simple terms, how they are currently being used by academia and industry alike with real-world examples of such.


The author would like to thank Anthony Platanios, Doctoral Researcher with the Machine Learning Department at Carnegie Mellon University for constructive criticism, along editorial comments in preparation of this article.

DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University, nor other companies (directly or indirectly) associated with the author(s). These writings are not intended to be final products, yet rather a reflection of current thinking, along being a catalyst for discussion and improvement.

Don't forget to share

You may also like...

Leave a Reply

Your email address will not be published. Required fields are marked *